# Location Anomalies Detection for Connected and Autonomous Vehicles

## TL;DR

This paper introduces an unsupervised deep autoencoder model to detect location anomalies in connected vehicles, enhancing safety and security in intelligent transportation systems.

## Contribution

It presents a novel deep autoencoder-based approach for anomaly detection in CAV location data, using RSSI and location features, with demonstrated effectiveness.

## Key findings

- Effective detection of location anomalies in simulated data
- Robustness of the model against different anomaly scenarios
- Potential for real-time anomaly monitoring in CAV systems

## Abstract

Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.